# TechnoSpark

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Jay Prajapati](https://cerebralvalley.ai/u/jay9122)
- **GitHub:** https://github.com/Jay2219/KernelFordge
- **Demo video:** https://youtu.be/lTLYDouNGCw
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5049

Modern Large Language Models spend up to 70% of their execution time stalled on GPU memory bandwidth. While PyTorch eager execution triggers intermediate High Bandwidth Memory (HBM) roundtrips for every simple operation, writing custom OpenAI Triton or CUDA C++ kernels requires scarce compiler engineering expertise.

KernelForge is an autonomous, multi-agent compiler that ingests raw PyTorch operations, mathematically analyzes their operational intensity against the target GPU's memory roofline, and synthesizes memory-coalesced, fused OpenAI Triton kernels (@triton.jit).

Unlike static code generators, KernelForge operates a closed-loop compilation and profiling harness:
1. Architect (Muse Wonder): Analyzes tensor AST and GPU hardware specs (NVIDIA A10G / T4) to calculate arithmetic intensity (I = FLOPs/Byte) and plan shared-memory tiling bounds (BLOCK_SIZE, warps, stages).
2. Synthesizer (Muse Code / Muse Spark 1.3): Translates the tiling plan into memory-coalesced @triton.jit source code with FP32 accumulator promotion and dynamic stride handling.
3. Auditor (Meta Open-Weights: Muse Spark Open on Modal): Deployed on Modal serverless GPUs, synthesizing property tests across tensor shape permutations and injecting numerical assertions (torch.testing.assert_close).
4. Subprocess-Isolated Sandbox: Executes kernels inside an isolated child process with CUDA_LAUNCH_BLOCKING=1 and a 20s watchdog, preventing CUDA driver context poisoning on memory faults.
5. Reflection Debugger (Agentic Self-Healing): Catches illegal memory accesses or numerical drift, parses the CUDA traceback, and autonomously patches boundary masks within 4 seconds.

Empirical Benchmarks (Stanford KernelBench & LLaMA-3 Production Primitives):
Evaluated following the methodology of Stanford's KernelBench (Level 1 & Level 2) and LLaMA-3 production workloads using triton.testing.do_bench:
• Level-1 LLaMA-3 Fused RMSNorm: 2.86x speedup vs. eager, 1.24x vs. compile (86.8% memory bus saturation).
• Level-1 Fused SwiGLU: 2.45x speedup vs. eager, 1.20x vs. compile (84.5% memory bus saturation).
• Level-1 Safe Online Softmax: 2.41x speedup vs. eager, 1.22x vs. compile (82.0% memory bus saturation).
• Level-2 Fused ResAdd + RMSNorm (Boss Problem): 3.79x speedup vs. eager, 1.42x vs. compile (91.4% memory bus saturation).
• Self-Healing Stress Test (N=4097): Automatically caught unaligned pointer trap and self-corrected to 100% numerical parity (Pass@2).

KernelForge is fully open-source with reproducible JSON artifacts and formal academic citations.

## More from Meta Global AI Developer Hackathon

- [Jaya Prakash Merepala](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5046)
- [Mimic](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5047)
- [LastSeen AI](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5048)
- [Team Umi Zoomi](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5050)
- [Arca Computer](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5051)
- [Markedia](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/5052)

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